Adding & Removing Columns¶

In [1]:

import pandas as pdhouses = pd.read_csv("data/kc_house_data.csv")titanic = pd.read_csv("data/titanic.csv")netflix = pd.read_csv("data/netflix_titles.csv", sep="|", index_col=0)btc = pd.read_csv("data/coin_Bitcoin.csv")countries = pd.read_csv("data/world-happiness-report-2021.csv")

In [2]:

countries.set_index("Country name", inplace=True)

Dropping Rows/Cols¶

In [3]:

btc.drop(labels="Symbol", axis=1)

Out[3]:

	SNo	Name	Date	High	Low	Open	Close	Volume	Marketcap
0	1	Bitcoin	2013-04-29 23:59:59	147.488007	134.000000	134.444000	144.539993	0.000000e+00	1.603769e+09
1	2	Bitcoin	2013-04-30 23:59:59	146.929993	134.050003	144.000000	139.000000	0.000000e+00	1.542813e+09
2	3	Bitcoin	2013-05-01 23:59:59	139.889999	107.720001	139.000000	116.989998	0.000000e+00	1.298955e+09
3	4	Bitcoin	2013-05-02 23:59:59	125.599998	92.281898	116.379997	105.209999	0.000000e+00	1.168517e+09
4	5	Bitcoin	2013-05-03 23:59:59	108.127998	79.099998	106.250000	97.750000	0.000000e+00	1.085995e+09
...	...	...	...	...	...	...	...	...	...
2986	2987	Bitcoin	2021-07-02 23:59:59	33939.588699	32770.680780	33549.600177	33897.048590	3.872897e+10	6.354508e+11
2987	2988	Bitcoin	2021-07-03 23:59:59	34909.259899	33402.696536	33854.421362	34668.548402	2.438396e+10	6.499397e+11
2988	2989	Bitcoin	2021-07-04 23:59:59	35937.567147	34396.477458	34665.564866	35287.779766	2.492431e+10	6.615748e+11
2989	2990	Bitcoin	2021-07-05 23:59:59	35284.344430	33213.661034	35284.344430	33746.002456	2.672155e+10	6.326962e+11
2990	2991	Bitcoin	2021-07-06 23:59:59	35038.536363	33599.916169	33723.509655	34235.193451	2.650126e+10	6.418992e+11

2991 rows × 9 columns

In [4]:

btc

Out[4]:

	SNo	Name	Symbol	Date	High	Low	Open	Close	Volume	Marketcap
0	1	Bitcoin	BTC	2013-04-29 23:59:59	147.488007	134.000000	134.444000	144.539993	0.000000e+00	1.603769e+09
1	2	Bitcoin	BTC	2013-04-30 23:59:59	146.929993	134.050003	144.000000	139.000000	0.000000e+00	1.542813e+09
2	3	Bitcoin	BTC	2013-05-01 23:59:59	139.889999	107.720001	139.000000	116.989998	0.000000e+00	1.298955e+09
3	4	Bitcoin	BTC	2013-05-02 23:59:59	125.599998	92.281898	116.379997	105.209999	0.000000e+00	1.168517e+09
4	5	Bitcoin	BTC	2013-05-03 23:59:59	108.127998	79.099998	106.250000	97.750000	0.000000e+00	1.085995e+09
...	...	...	...	...	...	...	...	...	...	...
2986	2987	Bitcoin	BTC	2021-07-02 23:59:59	33939.588699	32770.680780	33549.600177	33897.048590	3.872897e+10	6.354508e+11
2987	2988	Bitcoin	BTC	2021-07-03 23:59:59	34909.259899	33402.696536	33854.421362	34668.548402	2.438396e+10	6.499397e+11
2988	2989	Bitcoin	BTC	2021-07-04 23:59:59	35937.567147	34396.477458	34665.564866	35287.779766	2.492431e+10	6.615748e+11
2989	2990	Bitcoin	BTC	2021-07-05 23:59:59	35284.344430	33213.661034	35284.344430	33746.002456	2.672155e+10	6.326962e+11
2990	2991	Bitcoin	BTC	2021-07-06 23:59:59	35038.536363	33599.916169	33723.509655	34235.193451	2.650126e+10	6.418992e+11

2991 rows × 10 columns

In [5]:

btc.drop(labels=["SNo", "Name", "Symbol"], axis='columns')

Out[5]:

	Date	High	Low	Open	Close	Volume	Marketcap
0	2013-04-29 23:59:59	147.488007	134.000000	134.444000	144.539993	0.000000e+00	1.603769e+09
1	2013-04-30 23:59:59	146.929993	134.050003	144.000000	139.000000	0.000000e+00	1.542813e+09
2	2013-05-01 23:59:59	139.889999	107.720001	139.000000	116.989998	0.000000e+00	1.298955e+09
3	2013-05-02 23:59:59	125.599998	92.281898	116.379997	105.209999	0.000000e+00	1.168517e+09
4	2013-05-03 23:59:59	108.127998	79.099998	106.250000	97.750000	0.000000e+00	1.085995e+09
...	...	...	...	...	...	...	...
2986	2021-07-02 23:59:59	33939.588699	32770.680780	33549.600177	33897.048590	3.872897e+10	6.354508e+11
2987	2021-07-03 23:59:59	34909.259899	33402.696536	33854.421362	34668.548402	2.438396e+10	6.499397e+11
2988	2021-07-04 23:59:59	35937.567147	34396.477458	34665.564866	35287.779766	2.492431e+10	6.615748e+11
2989	2021-07-05 23:59:59	35284.344430	33213.661034	35284.344430	33746.002456	2.672155e+10	6.326962e+11
2990	2021-07-06 23:59:59	35038.536363	33599.916169	33723.509655	34235.193451	2.650126e+10	6.418992e+11

2991 rows × 7 columns

In [6]:

# btc.drop(labels=["SNo", "Name", "Symbol"], axis='columns')btc.drop(columns=["SNo", "Name", "Symbol"])

Out[6]:

	Date	High	Low	Open	Close	Volume	Marketcap
0	2013-04-29 23:59:59	147.488007	134.000000	134.444000	144.539993	0.000000e+00	1.603769e+09
1	2013-04-30 23:59:59	146.929993	134.050003	144.000000	139.000000	0.000000e+00	1.542813e+09
2	2013-05-01 23:59:59	139.889999	107.720001	139.000000	116.989998	0.000000e+00	1.298955e+09
3	2013-05-02 23:59:59	125.599998	92.281898	116.379997	105.209999	0.000000e+00	1.168517e+09
4	2013-05-03 23:59:59	108.127998	79.099998	106.250000	97.750000	0.000000e+00	1.085995e+09
...	...	...	...	...	...	...	...
2986	2021-07-02 23:59:59	33939.588699	32770.680780	33549.600177	33897.048590	3.872897e+10	6.354508e+11
2987	2021-07-03 23:59:59	34909.259899	33402.696536	33854.421362	34668.548402	2.438396e+10	6.499397e+11
2988	2021-07-04 23:59:59	35937.567147	34396.477458	34665.564866	35287.779766	2.492431e+10	6.615748e+11
2989	2021-07-05 23:59:59	35284.344430	33213.661034	35284.344430	33746.002456	2.672155e+10	6.326962e+11
2990	2021-07-06 23:59:59	35038.536363	33599.916169	33723.509655	34235.193451	2.650126e+10	6.418992e+11

2991 rows × 7 columns

In [7]:

btc[["Date", "High", "Low"]]

Out[7]:

	Date	High	Low
0	2013-04-29 23:59:59	147.488007	134.000000
1	2013-04-30 23:59:59	146.929993	134.050003
2	2013-05-01 23:59:59	139.889999	107.720001
3	2013-05-02 23:59:59	125.599998	92.281898
4	2013-05-03 23:59:59	108.127998	79.099998
...	...	...	...
2986	2021-07-02 23:59:59	33939.588699	32770.680780
2987	2021-07-03 23:59:59	34909.259899	33402.696536
2988	2021-07-04 23:59:59	35937.567147	34396.477458
2989	2021-07-05 23:59:59	35284.344430	33213.661034
2990	2021-07-06 23:59:59	35038.536363	33599.916169

2991 rows × 3 columns

In [8]:

btc.drop(columns=["SNo", "Name", "Symbol"], inplace=True)

In [9]:

btc

Out[9]:

	Date	High	Low	Open	Close	Volume	Marketcap
0	2013-04-29 23:59:59	147.488007	134.000000	134.444000	144.539993	0.000000e+00	1.603769e+09
1	2013-04-30 23:59:59	146.929993	134.050003	144.000000	139.000000	0.000000e+00	1.542813e+09
2	2013-05-01 23:59:59	139.889999	107.720001	139.000000	116.989998	0.000000e+00	1.298955e+09
3	2013-05-02 23:59:59	125.599998	92.281898	116.379997	105.209999	0.000000e+00	1.168517e+09
4	2013-05-03 23:59:59	108.127998	79.099998	106.250000	97.750000	0.000000e+00	1.085995e+09
...	...	...	...	...	...	...	...
2986	2021-07-02 23:59:59	33939.588699	32770.680780	33549.600177	33897.048590	3.872897e+10	6.354508e+11
2987	2021-07-03 23:59:59	34909.259899	33402.696536	33854.421362	34668.548402	2.438396e+10	6.499397e+11
2988	2021-07-04 23:59:59	35937.567147	34396.477458	34665.564866	35287.779766	2.492431e+10	6.615748e+11
2989	2021-07-05 23:59:59	35284.344430	33213.661034	35284.344430	33746.002456	2.672155e+10	6.326962e+11
2990	2021-07-06 23:59:59	35038.536363	33599.916169	33723.509655	34235.193451	2.650126e+10	6.418992e+11

2991 rows × 7 columns

In [10]:

countries

Out[10]:

	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
Country name
Finland	Western Europe	7.842	0.032	7.904	7.780	10.775	0.954	72.000	0.949	-0.098	0.186	2.43	1.446	1.106	0.741	0.691	0.124	0.481	3.253
Denmark	Western Europe	7.620	0.035	7.687	7.552	10.933	0.954	72.700	0.946	0.030	0.179	2.43	1.502	1.108	0.763	0.686	0.208	0.485	2.868
Switzerland	Western Europe	7.571	0.036	7.643	7.500	11.117	0.942	74.400	0.919	0.025	0.292	2.43	1.566	1.079	0.816	0.653	0.204	0.413	2.839
Iceland	Western Europe	7.554	0.059	7.670	7.438	10.878	0.983	73.000	0.955	0.160	0.673	2.43	1.482	1.172	0.772	0.698	0.293	0.170	2.967
Netherlands	Western Europe	7.464	0.027	7.518	7.410	10.932	0.942	72.400	0.913	0.175	0.338	2.43	1.501	1.079	0.753	0.647	0.302	0.384	2.798
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Lesotho	Sub-Saharan Africa	3.512	0.120	3.748	3.276	7.926	0.787	48.700	0.715	-0.131	0.915	2.43	0.451	0.731	0.007	0.405	0.103	0.015	1.800
Botswana	Sub-Saharan Africa	3.467	0.074	3.611	3.322	9.782	0.784	59.269	0.824	-0.246	0.801	2.43	1.099	0.724	0.340	0.539	0.027	0.088	0.648
Rwanda	Sub-Saharan Africa	3.415	0.068	3.548	3.282	7.676	0.552	61.400	0.897	0.061	0.167	2.43	0.364	0.202	0.407	0.627	0.227	0.493	1.095
Zimbabwe	Sub-Saharan Africa	3.145	0.058	3.259	3.030	7.943	0.750	56.201	0.677	-0.047	0.821	2.43	0.457	0.649	0.243	0.359	0.157	0.075	1.205
Afghanistan	South Asia	2.523	0.038	2.596	2.449	7.695	0.463	52.493	0.382	-0.102	0.924	2.43	0.370	0.000	0.126	0.000	0.122	0.010	1.895

149 rows × 19 columns

In [11]:

countries.drop(labels="Denmark", axis=0)

Out[11]:

	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
Country name
Finland	Western Europe	7.842	0.032	7.904	7.780	10.775	0.954	72.000	0.949	-0.098	0.186	2.43	1.446	1.106	0.741	0.691	0.124	0.481	3.253
Switzerland	Western Europe	7.571	0.036	7.643	7.500	11.117	0.942	74.400	0.919	0.025	0.292	2.43	1.566	1.079	0.816	0.653	0.204	0.413	2.839
Iceland	Western Europe	7.554	0.059	7.670	7.438	10.878	0.983	73.000	0.955	0.160	0.673	2.43	1.482	1.172	0.772	0.698	0.293	0.170	2.967
Netherlands	Western Europe	7.464	0.027	7.518	7.410	10.932	0.942	72.400	0.913	0.175	0.338	2.43	1.501	1.079	0.753	0.647	0.302	0.384	2.798
Norway	Western Europe	7.392	0.035	7.462	7.323	11.053	0.954	73.300	0.960	0.093	0.270	2.43	1.543	1.108	0.782	0.703	0.249	0.427	2.580
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Lesotho	Sub-Saharan Africa	3.512	0.120	3.748	3.276	7.926	0.787	48.700	0.715	-0.131	0.915	2.43	0.451	0.731	0.007	0.405	0.103	0.015	1.800
Botswana	Sub-Saharan Africa	3.467	0.074	3.611	3.322	9.782	0.784	59.269	0.824	-0.246	0.801	2.43	1.099	0.724	0.340	0.539	0.027	0.088	0.648
Rwanda	Sub-Saharan Africa	3.415	0.068	3.548	3.282	7.676	0.552	61.400	0.897	0.061	0.167	2.43	0.364	0.202	0.407	0.627	0.227	0.493	1.095
Zimbabwe	Sub-Saharan Africa	3.145	0.058	3.259	3.030	7.943	0.750	56.201	0.677	-0.047	0.821	2.43	0.457	0.649	0.243	0.359	0.157	0.075	1.205
Afghanistan	South Asia	2.523	0.038	2.596	2.449	7.695	0.463	52.493	0.382	-0.102	0.924	2.43	0.370	0.000	0.126	0.000	0.122	0.010	1.895

148 rows × 19 columns

In [12]:

countries

Out[12]:

	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
Country name
Finland	Western Europe	7.842	0.032	7.904	7.780	10.775	0.954	72.000	0.949	-0.098	0.186	2.43	1.446	1.106	0.741	0.691	0.124	0.481	3.253
Denmark	Western Europe	7.620	0.035	7.687	7.552	10.933	0.954	72.700	0.946	0.030	0.179	2.43	1.502	1.108	0.763	0.686	0.208	0.485	2.868
Switzerland	Western Europe	7.571	0.036	7.643	7.500	11.117	0.942	74.400	0.919	0.025	0.292	2.43	1.566	1.079	0.816	0.653	0.204	0.413	2.839
Iceland	Western Europe	7.554	0.059	7.670	7.438	10.878	0.983	73.000	0.955	0.160	0.673	2.43	1.482	1.172	0.772	0.698	0.293	0.170	2.967
Netherlands	Western Europe	7.464	0.027	7.518	7.410	10.932	0.942	72.400	0.913	0.175	0.338	2.43	1.501	1.079	0.753	0.647	0.302	0.384	2.798
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Lesotho	Sub-Saharan Africa	3.512	0.120	3.748	3.276	7.926	0.787	48.700	0.715	-0.131	0.915	2.43	0.451	0.731	0.007	0.405	0.103	0.015	1.800
Botswana	Sub-Saharan Africa	3.467	0.074	3.611	3.322	9.782	0.784	59.269	0.824	-0.246	0.801	2.43	1.099	0.724	0.340	0.539	0.027	0.088	0.648
Rwanda	Sub-Saharan Africa	3.415	0.068	3.548	3.282	7.676	0.552	61.400	0.897	0.061	0.167	2.43	0.364	0.202	0.407	0.627	0.227	0.493	1.095
Zimbabwe	Sub-Saharan Africa	3.145	0.058	3.259	3.030	7.943	0.750	56.201	0.677	-0.047	0.821	2.43	0.457	0.649	0.243	0.359	0.157	0.075	1.205
Afghanistan	South Asia	2.523	0.038	2.596	2.449	7.695	0.463	52.493	0.382	-0.102	0.924	2.43	0.370	0.000	0.126	0.000	0.122	0.010	1.895

149 rows × 19 columns

In [13]:

countries.drop(["Denmark", "Iceland", "Finland"])

Out[13]:

	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
Country name
Switzerland	Western Europe	7.571	0.036	7.643	7.500	11.117	0.942	74.400	0.919	0.025	0.292	2.43	1.566	1.079	0.816	0.653	0.204	0.413	2.839
Netherlands	Western Europe	7.464	0.027	7.518	7.410	10.932	0.942	72.400	0.913	0.175	0.338	2.43	1.501	1.079	0.753	0.647	0.302	0.384	2.798
Norway	Western Europe	7.392	0.035	7.462	7.323	11.053	0.954	73.300	0.960	0.093	0.270	2.43	1.543	1.108	0.782	0.703	0.249	0.427	2.580
Sweden	Western Europe	7.363	0.036	7.433	7.293	10.867	0.934	72.700	0.945	0.086	0.237	2.43	1.478	1.062	0.763	0.685	0.244	0.448	2.683
Luxembourg	Western Europe	7.324	0.037	7.396	7.252	11.647	0.908	72.600	0.907	-0.034	0.386	2.43	1.751	1.003	0.760	0.639	0.166	0.353	2.653
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Lesotho	Sub-Saharan Africa	3.512	0.120	3.748	3.276	7.926	0.787	48.700	0.715	-0.131	0.915	2.43	0.451	0.731	0.007	0.405	0.103	0.015	1.800
Botswana	Sub-Saharan Africa	3.467	0.074	3.611	3.322	9.782	0.784	59.269	0.824	-0.246	0.801	2.43	1.099	0.724	0.340	0.539	0.027	0.088	0.648
Rwanda	Sub-Saharan Africa	3.415	0.068	3.548	3.282	7.676	0.552	61.400	0.897	0.061	0.167	2.43	0.364	0.202	0.407	0.627	0.227	0.493	1.095
Zimbabwe	Sub-Saharan Africa	3.145	0.058	3.259	3.030	7.943	0.750	56.201	0.677	-0.047	0.821	2.43	0.457	0.649	0.243	0.359	0.157	0.075	1.205
Afghanistan	South Asia	2.523	0.038	2.596	2.449	7.695	0.463	52.493	0.382	-0.102	0.924	2.43	0.370	0.000	0.126	0.000	0.122	0.010	1.895

146 rows × 19 columns

In [14]:

btc.sort_index(ascending=False, inplace=True)btc.drop(2990)

Out[14]:

	Date	High	Low	Open	Close	Volume	Marketcap
2989	2021-07-05 23:59:59	35284.344430	33213.661034	35284.344430	33746.002456	2.672155e+10	6.326962e+11
2988	2021-07-04 23:59:59	35937.567147	34396.477458	34665.564866	35287.779766	2.492431e+10	6.615748e+11
2987	2021-07-03 23:59:59	34909.259899	33402.696536	33854.421362	34668.548402	2.438396e+10	6.499397e+11
2986	2021-07-02 23:59:59	33939.588699	32770.680780	33549.600177	33897.048590	3.872897e+10	6.354508e+11
2985	2021-07-01 23:59:59	35035.982712	32883.781226	35035.982712	33572.117653	3.783896e+10	6.293393e+11
...	...	...	...	...	...	...	...
4	2013-05-03 23:59:59	108.127998	79.099998	106.250000	97.750000	0.000000e+00	1.085995e+09
3	2013-05-02 23:59:59	125.599998	92.281898	116.379997	105.209999	0.000000e+00	1.168517e+09
2	2013-05-01 23:59:59	139.889999	107.720001	139.000000	116.989998	0.000000e+00	1.298955e+09
1	2013-04-30 23:59:59	146.929993	134.050003	144.000000	139.000000	0.000000e+00	1.542813e+09
0	2013-04-29 23:59:59	147.488007	134.000000	134.444000	144.539993	0.000000e+00	1.603769e+09

2990 rows × 7 columns

In [15]:

countries

Out[15]:

	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
Country name
Finland	Western Europe	7.842	0.032	7.904	7.780	10.775	0.954	72.000	0.949	-0.098	0.186	2.43	1.446	1.106	0.741	0.691	0.124	0.481	3.253
Denmark	Western Europe	7.620	0.035	7.687	7.552	10.933	0.954	72.700	0.946	0.030	0.179	2.43	1.502	1.108	0.763	0.686	0.208	0.485	2.868
Switzerland	Western Europe	7.571	0.036	7.643	7.500	11.117	0.942	74.400	0.919	0.025	0.292	2.43	1.566	1.079	0.816	0.653	0.204	0.413	2.839
Iceland	Western Europe	7.554	0.059	7.670	7.438	10.878	0.983	73.000	0.955	0.160	0.673	2.43	1.482	1.172	0.772	0.698	0.293	0.170	2.967
Netherlands	Western Europe	7.464	0.027	7.518	7.410	10.932	0.942	72.400	0.913	0.175	0.338	2.43	1.501	1.079	0.753	0.647	0.302	0.384	2.798
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Lesotho	Sub-Saharan Africa	3.512	0.120	3.748	3.276	7.926	0.787	48.700	0.715	-0.131	0.915	2.43	0.451	0.731	0.007	0.405	0.103	0.015	1.800
Botswana	Sub-Saharan Africa	3.467	0.074	3.611	3.322	9.782	0.784	59.269	0.824	-0.246	0.801	2.43	1.099	0.724	0.340	0.539	0.027	0.088	0.648
Rwanda	Sub-Saharan Africa	3.415	0.068	3.548	3.282	7.676	0.552	61.400	0.897	0.061	0.167	2.43	0.364	0.202	0.407	0.627	0.227	0.493	1.095
Zimbabwe	Sub-Saharan Africa	3.145	0.058	3.259	3.030	7.943	0.750	56.201	0.677	-0.047	0.821	2.43	0.457	0.649	0.243	0.359	0.157	0.075	1.205
Afghanistan	South Asia	2.523	0.038	2.596	2.449	7.695	0.463	52.493	0.382	-0.102	0.924	2.43	0.370	0.000	0.126	0.000	0.122	0.010	1.895

149 rows × 19 columns

In [16]:

countries.drop(countries.index[0])

Out[16]:

	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
Country name
Denmark	Western Europe	7.620	0.035	7.687	7.552	10.933	0.954	72.700	0.946	0.030	0.179	2.43	1.502	1.108	0.763	0.686	0.208	0.485	2.868
Switzerland	Western Europe	7.571	0.036	7.643	7.500	11.117	0.942	74.400	0.919	0.025	0.292	2.43	1.566	1.079	0.816	0.653	0.204	0.413	2.839
Iceland	Western Europe	7.554	0.059	7.670	7.438	10.878	0.983	73.000	0.955	0.160	0.673	2.43	1.482	1.172	0.772	0.698	0.293	0.170	2.967
Netherlands	Western Europe	7.464	0.027	7.518	7.410	10.932	0.942	72.400	0.913	0.175	0.338	2.43	1.501	1.079	0.753	0.647	0.302	0.384	2.798
Norway	Western Europe	7.392	0.035	7.462	7.323	11.053	0.954	73.300	0.960	0.093	0.270	2.43	1.543	1.108	0.782	0.703	0.249	0.427	2.580
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Lesotho	Sub-Saharan Africa	3.512	0.120	3.748	3.276	7.926	0.787	48.700	0.715	-0.131	0.915	2.43	0.451	0.731	0.007	0.405	0.103	0.015	1.800
Botswana	Sub-Saharan Africa	3.467	0.074	3.611	3.322	9.782	0.784	59.269	0.824	-0.246	0.801	2.43	1.099	0.724	0.340	0.539	0.027	0.088	0.648
Rwanda	Sub-Saharan Africa	3.415	0.068	3.548	3.282	7.676	0.552	61.400	0.897	0.061	0.167	2.43	0.364	0.202	0.407	0.627	0.227	0.493	1.095
Zimbabwe	Sub-Saharan Africa	3.145	0.058	3.259	3.030	7.943	0.750	56.201	0.677	-0.047	0.821	2.43	0.457	0.649	0.243	0.359	0.157	0.075	1.205
Afghanistan	South Asia	2.523	0.038	2.596	2.449	7.695	0.463	52.493	0.382	-0.102	0.924	2.43	0.370	0.000	0.126	0.000	0.122	0.010	1.895

148 rows × 19 columns

In [17]:

countries.drop(countries.index[10:])

Out[17]:

	Regional indicator	Ladder score	Standard error of ladder score	upperwhisker	lowerwhisker	Logged GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Ladder score in Dystopia	Explained by: Log GDP per capita	Explained by: Social support	Explained by: Healthy life expectancy	Explained by: Freedom to make life choices	Explained by: Generosity	Explained by: Perceptions of corruption	Dystopia + residual
Country name
Finland	Western Europe	7.842	0.032	7.904	7.780	10.775	0.954	72.0	0.949	-0.098	0.186	2.43	1.446	1.106	0.741	0.691	0.124	0.481	3.253
Denmark	Western Europe	7.620	0.035	7.687	7.552	10.933	0.954	72.7	0.946	0.030	0.179	2.43	1.502	1.108	0.763	0.686	0.208	0.485	2.868
Switzerland	Western Europe	7.571	0.036	7.643	7.500	11.117	0.942	74.4	0.919	0.025	0.292	2.43	1.566	1.079	0.816	0.653	0.204	0.413	2.839
Iceland	Western Europe	7.554	0.059	7.670	7.438	10.878	0.983	73.0	0.955	0.160	0.673	2.43	1.482	1.172	0.772	0.698	0.293	0.170	2.967
Netherlands	Western Europe	7.464	0.027	7.518	7.410	10.932	0.942	72.4	0.913	0.175	0.338	2.43	1.501	1.079	0.753	0.647	0.302	0.384	2.798
Norway	Western Europe	7.392	0.035	7.462	7.323	11.053	0.954	73.3	0.960	0.093	0.270	2.43	1.543	1.108	0.782	0.703	0.249	0.427	2.580
Sweden	Western Europe	7.363	0.036	7.433	7.293	10.867	0.934	72.7	0.945	0.086	0.237	2.43	1.478	1.062	0.763	0.685	0.244	0.448	2.683
Luxembourg	Western Europe	7.324	0.037	7.396	7.252	11.647	0.908	72.6	0.907	-0.034	0.386	2.43	1.751	1.003	0.760	0.639	0.166	0.353	2.653
New Zealand	North America and ANZ	7.277	0.040	7.355	7.198	10.643	0.948	73.4	0.929	0.134	0.242	2.43	1.400	1.094	0.785	0.665	0.276	0.445	2.612
Austria	Western Europe	7.268	0.036	7.337	7.198	10.906	0.934	73.3	0.908	0.042	0.481	2.43	1.492	1.062	0.782	0.640	0.215	0.292	2.784

Adding Columns¶

In [18]:

titanic["species"] = "human"titanic

Out[18]:

	pclass	survived	name	sex	age	sibsp	parch	ticket	fare	cabin	embarked	boat	body	home.dest	species
0	1	1	Allen, Miss. Elisabeth Walton	female	29	0	0	24160	211.3375	B5	S	2	?	St Louis, MO	human
1	1	1	Allison, Master. Hudson Trevor	male	0.9167	1	2	113781	151.55	C22 C26	S	11	?	Montreal, PQ / Chesterville, ON	human
2	1	0	Allison, Miss. Helen Loraine	female	2	1	2	113781	151.55	C22 C26	S	?	?	Montreal, PQ / Chesterville, ON	human
3	1	0	Allison, Mr. Hudson Joshua Creighton	male	30	1	2	113781	151.55	C22 C26	S	?	135	Montreal, PQ / Chesterville, ON	human
4	1	0	Allison, Mrs. Hudson J C (Bessie Waldo Daniels)	female	25	1	2	113781	151.55	C22 C26	S	?	?	Montreal, PQ / Chesterville, ON	human
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1304	3	0	Zabour, Miss. Hileni	female	14.5	1	0	2665	14.4542	?	C	?	328	?	human
1305	3	0	Zabour, Miss. Thamine	female	?	1	0	2665	14.4542	?	C	?	?	?	human
1306	3	0	Zakarian, Mr. Mapriededer	male	26.5	0	0	2656	7.225	?	C	?	304	?	human
1307	3	0	Zakarian, Mr. Ortin	male	27	0	0	2670	7.225	?	C	?	?	?	human
1308	3	0	Zimmerman, Mr. Leo	male	29	0	0	315082	7.875	?	S	?	?	?	human

1309 rows × 15 columns

In [19]:

houses.insert(0, "county", "King County")

In [20]:

houses

Out[20]:

	county	id	date	price	bedrooms	bathrooms	sqft_living	sqft_lot	floors	waterfront	...	grade	sqft_above	sqft_basement	yr_built	yr_renovated	zipcode	lat	long	sqft_living15	sqft_lot15
0	King County	7129300520	20141013T000000	221900.0	3	1.00	1180	5650	1.0	0	...	7	1180	0	1955	0	98178	47.5112	-122.257	1340	5650
1	King County	6414100192	20141209T000000	538000.0	3	2.25	2570	7242	2.0	0	...	7	2170	400	1951	1991	98125	47.7210	-122.319	1690	7639
2	King County	5631500400	20150225T000000	180000.0	2	1.00	770	10000	1.0	0	...	6	770	0	1933	0	98028	47.7379	-122.233	2720	8062
3	King County	2487200875	20141209T000000	604000.0	4	3.00	1960	5000	1.0	0	...	7	1050	910	1965	0	98136	47.5208	-122.393	1360	5000
4	King County	1954400510	20150218T000000	510000.0	3	2.00	1680	8080	1.0	0	...	8	1680	0	1987	0	98074	47.6168	-122.045	1800	7503
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
21608	King County	263000018	20140521T000000	360000.0	3	2.50	1530	1131	3.0	0	...	8	1530	0	2009	0	98103	47.6993	-122.346	1530	1509
21609	King County	6600060120	20150223T000000	400000.0	4	2.50	2310	5813	2.0	0	...	8	2310	0	2014	0	98146	47.5107	-122.362	1830	7200
21610	King County	1523300141	20140623T000000	402101.0	2	0.75	1020	1350	2.0	0	...	7	1020	0	2009	0	98144	47.5944	-122.299	1020	2007
21611	King County	291310100	20150116T000000	400000.0	3	2.50	1600	2388	2.0	0	...	8	1600	0	2004	0	98027	47.5345	-122.069	1410	1287
21612	King County	1523300157	20141015T000000	325000.0	2	0.75	1020	1076	2.0	0	...	7	1020	0	2008	0	98144	47.5941	-122.299	1020	1357

21613 rows × 22 columns

In [21]:

houses["sale_price"] = houses["price"]

In [22]:

houses

Out[22]:

	county	id	date	price	bedrooms	bathrooms	sqft_living	sqft_lot	floors	waterfront	...	sqft_above	sqft_basement	yr_built	yr_renovated	zipcode	lat	long	sqft_living15	sqft_lot15	sale_price
0	King County	7129300520	20141013T000000	221900.0	3	1.00	1180	5650	1.0	0	...	1180	0	1955	0	98178	47.5112	-122.257	1340	5650	221900.0
1	King County	6414100192	20141209T000000	538000.0	3	2.25	2570	7242	2.0	0	...	2170	400	1951	1991	98125	47.7210	-122.319	1690	7639	538000.0
2	King County	5631500400	20150225T000000	180000.0	2	1.00	770	10000	1.0	0	...	770	0	1933	0	98028	47.7379	-122.233	2720	8062	180000.0
3	King County	2487200875	20141209T000000	604000.0	4	3.00	1960	5000	1.0	0	...	1050	910	1965	0	98136	47.5208	-122.393	1360	5000	604000.0
4	King County	1954400510	20150218T000000	510000.0	3	2.00	1680	8080	1.0	0	...	1680	0	1987	0	98074	47.6168	-122.045	1800	7503	510000.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
21608	King County	263000018	20140521T000000	360000.0	3	2.50	1530	1131	3.0	0	...	1530	0	2009	0	98103	47.6993	-122.346	1530	1509	360000.0
21609	King County	6600060120	20150223T000000	400000.0	4	2.50	2310	5813	2.0	0	...	2310	0	2014	0	98146	47.5107	-122.362	1830	7200	400000.0
21610	King County	1523300141	20140623T000000	402101.0	2	0.75	1020	1350	2.0	0	...	1020	0	2009	0	98144	47.5944	-122.299	1020	2007	402101.0
21611	King County	291310100	20150116T000000	400000.0	3	2.50	1600	2388	2.0	0	...	1600	0	2004	0	98027	47.5345	-122.069	1410	1287	400000.0
21612	King County	1523300157	20141015T000000	325000.0	2	0.75	1020	1076	2.0	0	...	1020	0	2008	0	98144	47.5941	-122.299	1020	1357	325000.0

21613 rows × 23 columns

In [23]:

houses.insert(3, "num_bedrooms", houses["bedrooms"])

In [24]:

houses

Out[24]:

	county	id	date	num_bedrooms	price	bedrooms	bathrooms	sqft_living	sqft_lot	floors	...	sqft_above	sqft_basement	yr_built	yr_renovated	zipcode	lat	long	sqft_living15	sqft_lot15	sale_price
0	King County	7129300520	20141013T000000	3	221900.0	3	1.00	1180	5650	1.0	...	1180	0	1955	0	98178	47.5112	-122.257	1340	5650	221900.0
1	King County	6414100192	20141209T000000	3	538000.0	3	2.25	2570	7242	2.0	...	2170	400	1951	1991	98125	47.7210	-122.319	1690	7639	538000.0
2	King County	5631500400	20150225T000000	2	180000.0	2	1.00	770	10000	1.0	...	770	0	1933	0	98028	47.7379	-122.233	2720	8062	180000.0
3	King County	2487200875	20141209T000000	4	604000.0	4	3.00	1960	5000	1.0	...	1050	910	1965	0	98136	47.5208	-122.393	1360	5000	604000.0
4	King County	1954400510	20150218T000000	3	510000.0	3	2.00	1680	8080	1.0	...	1680	0	1987	0	98074	47.6168	-122.045	1800	7503	510000.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
21608	King County	263000018	20140521T000000	3	360000.0	3	2.50	1530	1131	3.0	...	1530	0	2009	0	98103	47.6993	-122.346	1530	1509	360000.0
21609	King County	6600060120	20150223T000000	4	400000.0	4	2.50	2310	5813	2.0	...	2310	0	2014	0	98146	47.5107	-122.362	1830	7200	400000.0
21610	King County	1523300141	20140623T000000	2	402101.0	2	0.75	1020	1350	2.0	...	1020	0	2009	0	98144	47.5944	-122.299	1020	2007	402101.0
21611	King County	291310100	20150116T000000	3	400000.0	3	2.50	1600	2388	2.0	...	1600	0	2004	0	98027	47.5345	-122.069	1410	1287	400000.0
21612	King County	1523300157	20141015T000000	2	325000.0	2	0.75	1020	1076	2.0	...	1020	0	2008	0	98144	47.5941	-122.299	1020	1357	325000.0

21613 rows × 24 columns

In [25]:

titanic.drop(columns=["species"], inplace=True)

In [26]:

titanic["sibsp"]

Out[26]:

0 0

1 1

2 1

3 1

4 1

1304 1

1305 1

1306 0

1307 0

1308 0

Name: sibsp, Length: 1309, dtype: int64

In [27]:

titanic["parch"]

Out[27]:

0 0

1 2

2 2

3 2

4 2

1304 0

1305 0

1306 0

1307 0

1308 0

Name: parch, Length: 1309, dtype: int64

In [28]:

titanic["sibsp"] + titanic["parch"]

Out[28]:

0 0

1 3

2 3

3 3

4 3

1304 1

1305 1

1306 0

1307 0

1308 0

Length: 1309, dtype: int64

In [29]:

titanic["num_relatives"] = titanic["sibsp"] + titanic["parch"]

In [30]:

titanic

Out[30]:

	pclass	survived	name	sex	age	sibsp	parch	ticket	fare	cabin	embarked	boat	body	home.dest	num_relatives
0	1	1	Allen, Miss. Elisabeth Walton	female	29	0	0	24160	211.3375	B5	S	2	?	St Louis, MO	0
1	1	1	Allison, Master. Hudson Trevor	male	0.9167	1	2	113781	151.55	C22 C26	S	11	?	Montreal, PQ / Chesterville, ON	3
2	1	0	Allison, Miss. Helen Loraine	female	2	1	2	113781	151.55	C22 C26	S	?	?	Montreal, PQ / Chesterville, ON	3
3	1	0	Allison, Mr. Hudson Joshua Creighton	male	30	1	2	113781	151.55	C22 C26	S	?	135	Montreal, PQ / Chesterville, ON	3
4	1	0	Allison, Mrs. Hudson J C (Bessie Waldo Daniels)	female	25	1	2	113781	151.55	C22 C26	S	?	?	Montreal, PQ / Chesterville, ON	3
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1304	3	0	Zabour, Miss. Hileni	female	14.5	1	0	2665	14.4542	?	C	?	328	?	1
1305	3	0	Zabour, Miss. Thamine	female	?	1	0	2665	14.4542	?	C	?	?	?	1
1306	3	0	Zakarian, Mr. Mapriededer	male	26.5	0	0	2656	7.225	?	C	?	304	?	0
1307	3	0	Zakarian, Mr. Ortin	male	27	0	0	2670	7.225	?	C	?	?	?	0
1308	3	0	Zimmerman, Mr. Leo	male	29	0	0	315082	7.875	?	S	?	?	?	0

1309 rows × 15 columns

In [31]:

titanic.sort_values("num_relatives", ascending=False)

Out[31]:

	pclass	survived	name	sex	age	sibsp	parch	ticket	fare	cabin	embarked	boat	body	home.dest	num_relatives
1177	3	0	Sage, Mr. Frederick	male	?	8	2	CA. 2343	69.55	?	S	?	?	?	10
1179	3	0	Sage, Mr. John George	male	?	1	9	CA. 2343	69.55	?	S	?	?	?	10
1173	3	0	Sage, Miss. Constance Gladys	female	?	8	2	CA. 2343	69.55	?	S	?	?	?	10
1170	3	0	Sage, Master. Thomas Henry	male	?	8	2	CA. 2343	69.55	?	S	?	?	?	10
1172	3	0	Sage, Miss. Ada	female	?	8	2	CA. 2343	69.55	?	S	?	?	?	10
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
598	2	1	Wright, Miss. Marion	female	26	0	0	220844	13.5	?	S	9	?	Yoevil, England / Cottage Grove, OR	0
599	2	0	Yrois, Miss. Henriette ('Mrs Harbeck')	female	24	0	0	248747	13	?	S	?	?	Paris	0
600	3	0	Abbing, Mr. Anthony	male	42	0	0	C.A. 5547	7.55	?	S	?	?	?	0
604	3	1	Abelseth, Miss. Karen Marie	female	16	0	0	348125	7.65	?	S	16	?	Norway Los Angeles, CA	0
1308	3	0	Zimmerman, Mr. Leo	male	29	0	0	315082	7.875	?	S	?	?	?	0

1309 rows × 15 columns

In [32]:

titanic[titanic["survived"] == 1].sort_values("num_relatives", ascending=False)

Out[32]:

	pclass	survived	name	sex	age	sibsp	parch	ticket	fare	cabin	embarked	boat	body	home.dest	num_relatives
641	3	1	Asplund, Master. Edvin Rojj Felix	male	3	4	2	347077	31.3875	?	S	15	?	Sweden Worcester, MA	6
625	3	1	Andersson, Miss. Erna Alexandra	female	17	4	2	3101281	7.925	?	S	D	?	Ruotsinphyhtaa, Finland New York, NY	6
646	3	1	Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...	female	38	1	5	347077	31.3875	?	S	15	?	Sweden Worcester, MA	6
643	3	1	Asplund, Miss. Lillian Gertrud	female	5	4	2	347077	31.3875	?	S	15	?	Sweden Worcester, MA	6
111	1	1	Fortune, Miss. Alice Elizabeth	female	24	3	2	19950	263	C23 C25 C27	S	10	?	Winnipeg, MB	5
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
597	2	1	Williams, Mr. Charles Eugene	male	?	0	0	244373	13	?	S	14	?	Harrow, England	0
598	2	1	Wright, Miss. Marion	female	26	0	0	220844	13.5	?	S	9	?	Yoevil, England / Cottage Grove, OR	0
604	3	1	Abelseth, Miss. Karen Marie	female	16	0	0	348125	7.65	?	S	16	?	Norway Los Angeles, CA	0
605	3	1	Abelseth, Mr. Olaus Jorgensen	male	25	0	0	348122	7.65	F G63	S	A	?	Perkins County, SD	0
0	1	1	Allen, Miss. Elisabeth Walton	female	29	0	0	24160	211.3375	B5	S	2	?	St Louis, MO	0

500 rows × 15 columns

In [33]:

solo_passengers = titanic["num_relatives"] == 0titanic[solo_passengers].sex.value_counts().plot(kind="pie")

Out[33]:

<AxesSubplot:ylabel='sex'>

In [34]:

titanic.sex.value_counts().plot(kind="pie")

Out[34]:

<AxesSubplot:ylabel='sex'>

In [35]:

houses["price_sqft"] = houses["price"] / houses["sqft_living"]

In [36]:

houses

Out[36]:

	county	id	date	num_bedrooms	price	bedrooms	bathrooms	sqft_living	sqft_lot	floors	...	sqft_basement	yr_built	yr_renovated	zipcode	lat	long	sqft_living15	sqft_lot15	sale_price	price_sqft
0	King County	7129300520	20141013T000000	3	221900.0	3	1.00	1180	5650	1.0	...	0	1955	0	98178	47.5112	-122.257	1340	5650	221900.0	188.050847
1	King County	6414100192	20141209T000000	3	538000.0	3	2.25	2570	7242	2.0	...	400	1951	1991	98125	47.7210	-122.319	1690	7639	538000.0	209.338521
2	King County	5631500400	20150225T000000	2	180000.0	2	1.00	770	10000	1.0	...	0	1933	0	98028	47.7379	-122.233	2720	8062	180000.0	233.766234
3	King County	2487200875	20141209T000000	4	604000.0	4	3.00	1960	5000	1.0	...	910	1965	0	98136	47.5208	-122.393	1360	5000	604000.0	308.163265
4	King County	1954400510	20150218T000000	3	510000.0	3	2.00	1680	8080	1.0	...	0	1987	0	98074	47.6168	-122.045	1800	7503	510000.0	303.571429
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
21608	King County	263000018	20140521T000000	3	360000.0	3	2.50	1530	1131	3.0	...	0	2009	0	98103	47.6993	-122.346	1530	1509	360000.0	235.294118
21609	King County	6600060120	20150223T000000	4	400000.0	4	2.50	2310	5813	2.0	...	0	2014	0	98146	47.5107	-122.362	1830	7200	400000.0	173.160173
21610	King County	1523300141	20140623T000000	2	402101.0	2	0.75	1020	1350	2.0	...	0	2009	0	98144	47.5944	-122.299	1020	2007	402101.0	394.216667
21611	King County	291310100	20150116T000000	3	400000.0	3	2.50	1600	2388	2.0	...	0	2004	0	98027	47.5345	-122.069	1410	1287	400000.0	250.000000
21612	King County	1523300157	20141015T000000	2	325000.0	2	0.75	1020	1076	2.0	...	0	2008	0	98144	47.5941	-122.299	1020	1357	325000.0	318.627451

21613 rows × 25 columns

In [37]:

houses.sort_values("price_sqft", ascending=False)

Out[37]:

	county	id	date	num_bedrooms	price	bedrooms	bathrooms	sqft_living	sqft_lot	floors	...	sqft_basement	yr_built	yr_renovated	zipcode	lat	long	sqft_living15	sqft_lot15	sale_price	price_sqft
19336	King County	6021500970	20150407T000000	2	874950.0	2	1.00	1080	4000	1.0	...	0	1940	0	98117	47.6902	-122.387	1530	4240	874950.0	810.138889
4013	King County	724069059	20140509T000000	3	2400000.0	3	2.25	3000	11665	1.5	...	0	2001	0	98075	47.5884	-122.086	3000	15959	2400000.0	800.000000
10446	King County	1118000320	20150508T000000	4	3400000.0	4	4.00	4260	11765	2.0	...	980	1939	2010	98112	47.6380	-122.288	4260	10408	3400000.0	798.122066
8623	King County	6303400395	20150130T000000	1	325000.0	1	0.75	410	8636	1.0	...	0	1953	0	98146	47.5077	-122.357	1190	8636	325000.0	792.682927
9314	King County	4389200610	20141201T000000	2	903000.0	2	1.50	1140	7800	1.0	...	0	1947	0	98004	47.6142	-122.209	2020	7800	903000.0	792.105263
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1385	King County	3342700465	20150123T000000	3	250000.0	3	1.50	2840	10182	1.0	...	1330	1951	0	98056	47.5240	-122.200	2210	9669	250000.0	88.028169
17197	King County	5111400086	20140512T000000	3	110000.0	3	1.00	1250	53143	1.0	...	0	1945	0	98038	47.4235	-122.051	1820	217800	110000.0	88.000000
13825	King County	5637200450	20141017T000000	5	257000.0	5	2.75	2930	10148	2.0	...	0	2002	0	98059	47.4887	-122.145	2930	8425	257000.0	87.713311
3785	King County	723049156	20140523T000000	3	149000.0	3	1.00	1700	8645	1.0	...	0	1955	0	98146	47.4899	-122.337	1500	7980	149000.0	87.647059
18262	King County	2891000610	20141211T000000	4	148900.0	4	1.75	1700	6000	1.0	...	0	1967	0	98002	47.3252	-122.208	1280	6000	148900.0	87.588235

21613 rows × 25 columns

In [38]:

houses.sort_values("price_sqft", ascending=False).head(50)["zipcode"].value_counts().plot(kind="bar")

Out[38]:

<AxesSubplot:>

In [39]:

btc.set_index("Date")

Out[39]:

	High	Low	Open	Close	Volume	Marketcap
Date
2021-07-06 23:59:59	35038.536363	33599.916169	33723.509655	34235.193451	2.650126e+10	6.418992e+11
2021-07-05 23:59:59	35284.344430	33213.661034	35284.344430	33746.002456	2.672155e+10	6.326962e+11
2021-07-04 23:59:59	35937.567147	34396.477458	34665.564866	35287.779766	2.492431e+10	6.615748e+11
2021-07-03 23:59:59	34909.259899	33402.696536	33854.421362	34668.548402	2.438396e+10	6.499397e+11
2021-07-02 23:59:59	33939.588699	32770.680780	33549.600177	33897.048590	3.872897e+10	6.354508e+11
...	...	...	...	...	...	...
2013-05-03 23:59:59	108.127998	79.099998	106.250000	97.750000	0.000000e+00	1.085995e+09
2013-05-02 23:59:59	125.599998	92.281898	116.379997	105.209999	0.000000e+00	1.168517e+09
2013-05-01 23:59:59	139.889999	107.720001	139.000000	116.989998	0.000000e+00	1.298955e+09
2013-04-30 23:59:59	146.929993	134.050003	144.000000	139.000000	0.000000e+00	1.542813e+09
2013-04-29 23:59:59	147.488007	134.000000	134.444000	144.539993	0.000000e+00	1.603769e+09

2991 rows × 6 columns

In [40]:

btc["change"] = btc["Close"] - btc["Open"]

In [41]:

btc.sort_values("change", ascending=False)

Out[41]:

	Date	High	Low	Open	Close	Volume	Marketcap	change
2842	2021-02-08 23:59:59	46203.931437	38076.322807	38886.827290	46196.463719	1.014672e+11	8.603427e+11	7309.636429
2919	2021-04-26 23:59:59	54288.002155	48852.796843	49077.792363	54021.754787	5.828404e+10	1.009780e+12	4943.962424
2863	2021-03-01 23:59:59	49784.015290	45115.093115	45159.503053	49631.241371	5.389130e+10	9.252355e+11	4471.738318
2853	2021-02-19 23:59:59	56113.650547	50937.275722	51675.981285	55888.133682	6.349550e+10	1.041381e+12	4212.152397
2923	2021-04-30 23:59:59	57900.719988	53129.600877	53568.663584	57750.177346	5.239593e+10	1.079670e+12	4181.513762
...	...	...	...	...	...	...	...	...
2911	2021-04-18 23:59:59	61057.456509	52829.535926	60701.886093	56216.185002	9.746887e+10	1.050445e+12	-4485.701091
2824	2021-01-21 23:59:59	35552.679497	30250.749639	35549.397409	30825.698506	7.564307e+10	5.735657e+11	-4723.698903
2857	2021-02-23 23:59:59	54204.929756	45290.590268	54204.929756	48824.426869	1.061025e+11	9.099259e+11	-5380.502887
2942	2021-05-19 23:59:59	43546.116485	30681.496912	42944.975447	37002.440466	1.263581e+11	6.924526e+11	-5942.534981
2935	2021-05-12 23:59:59	57939.362415	49150.533875	56714.533167	49150.533875	7.521540e+10	9.195278e+11	-7563.999292

2991 rows × 8 columns

In [42]:

btc["delta"] = btc["High"] - btc["Low"]

In [43]:

btc.sort_values("delta", ascending=False)

Out[43]:

	Date	High	Low	Open	Close	Volume	Marketcap	change	delta
2942	2021-05-19 23:59:59	43546.116485	30681.496912	42944.975447	37002.440466	1.263581e+11	6.924526e+11	-5942.534981	12864.619573
2857	2021-02-23 23:59:59	54204.929756	45290.590268	54204.929756	48824.426869	1.061025e+11	9.099259e+11	-5380.502887	8914.339488
2935	2021-05-12 23:59:59	57939.362415	49150.533875	56714.533167	49150.533875	7.521540e+10	9.195278e+11	-7563.999292	8788.828540
2856	2021-02-22 23:59:59	57533.389325	48967.565188	57532.738864	54207.319065	9.205242e+10	1.010205e+12	-3325.419799	8565.824137
2944	2021-05-21 23:59:59	42172.173616	33616.453884	40596.948323	37304.690671	8.205162e+10	6.981088e+11	-3292.257651	8555.719732
...	...	...	...	...	...	...	...	...	...
871	2015-09-17 23:59:59	230.285004	228.925995	229.076004	229.809998	1.893540e+07	3.360247e+09	0.733994	1.359009
768	2015-06-06 23:59:59	225.718994	224.378998	225.005005	225.619003	1.113150e+07	3.213723e+09	0.613998	1.339996
759	2015-05-28 23:59:59	237.824005	236.651993	237.257004	237.408005	1.382960e+07	3.373817e+09	0.151001	1.172012
888	2015-10-04 23:59:59	238.968002	237.940002	238.531006	238.259003	1.299900e+07	3.499387e+09	-0.272003	1.028000
100	2013-08-07 23:59:59	106.750000	106.750000	106.750000	106.750000	0.000000e+00	1.229098e+09	0.000000	0.000000

2991 rows × 9 columns

In [44]:

btc.set_index("Date", inplace=True)

In [45]:

btc.sort_values("delta", ascending=False).head(10)["delta"].plot(kind="bar")

Out[45]:

<AxesSubplot:xlabel='Date'>